Among the topics covered in the report are the rise of payment platforms, the mobilization of money, and the advent of contactless payment in mobile commerce. This excerpt looks at the role big data is beginning to play in fraud detection for these services, and the new opportunities that development brings. Additional excerpts will be featured here on Radar throughout the week.

Web-native payment platforms have a tremendous challenge combating fraud — greater in complexity than that faced by traditional payment processors. But the solutions they have devised to deal with it have created some enormous new opportunities.

First, the problem: payment platforms have to process orders from many more sources than do credit card companies. “Traditional processors have to deal with tens of thousands of sources of fraud at each individual point-of-sale or merchant site,” said Matthew Mengerink, VP of Platform at PayPal. “PayPal has to be able to identify potential sources of fraud from the almost 90 million browsers and mobile phones that are constantly connecting to our payment processing services. We’re dealing with a much larger challenge, and we’ve designed systems to identify and manage fraudulent activity often before it has started.”

PayPal, Amazon, and Google have all developed sophisticated analytical tools and infrastructure to identify patterns of fraudulent activity. Paypal, for example, has a series of Fraud Management Filters that screen payments and sort out transactions that warrant review because of their amount, their origin, or other factors that can be set by a merchant. But the opportunity to identify fraud reaches far beyond this virtual point of sale. PayPal and Amazon have developed fraud detection tools that depend on massive datasets containing not only financial details for transactions, but IP addresses, browser information, and other technical data that will help these companies refine models to predict, identify, and prevent fraudulent activity. PayPal and Amazon have had years to amass databases of the transaction details for hundreds of millions of customers across thousands of merchants.

These tools vastly improve on the periodic, offline analysis that has been the norm. Institutions traditionally sampled existed data and ran nightly or weekly analyses using fraud-detection models. The newer approaches perform continuous, real-time analysis on large datasets, applying some of the lessons that Google and others have learned for indexing the web to the problem of calculating the risk of fraud for individual consumers or merchants. There’s a swarm of activity around a new crop of “big data” tools like Hadoop, MapReduce, and BigTable that can deal with huge amounts of data. The fraud question is a large driver of all this activity.

“Sampling is dead,” said Abhishek Mehta, a big data lead at a large U.S. bank institution. “When banks stored petabytes of information on magnetic tape, it was impossible for them to develop appropriate models to measure risk without resorting to sampling techniques. Today we can run analysis on upwards of 50 petabytes of data to more accurately calculate risk. Technologies such as Hadoop allow us to do things that were previously impossible.”

Mitigating risk is just one use for all this data. With everything that payment platforms know about their customers — transactions, searches, messages, likes and dislikes — they can increasingly use this information to devise sophisticated advertising models or predictive analytics for selling products and services. Privacy advocates might be alarmed, but the payment providers are just continuing a model pioneered by financial institutions decades ago for identifying consumer preferences and identifying fraud risks. The emergence of tools for processing big data creates new opportunities for payment platforms and vendors to get better at what they already do.

A payment system built on top of systems that facilitate real-time analytics creates some interesting possibilities. Consider the architecture of a modern advertising network like Google’s DoubleClick. DoubleClick and other ad networks have refined real-time auctions that deliver targeted ads to users in milliseconds. When a request for an ad comes in from a browser, it’s quickly passed to one or more advertisers, each of whom has between 10-20 milliseconds to match that user to a profile and assign a potential value to its bid. The high bidder gets to place its ad — and it all happens in under a second. These interactions are happening with every click, generating a massive amount of real-time modeling and calculations that drive an efficient market for advertising.

Imagine a similar system for electronic payments in which a payment platform offers potential transactions to competing credit issuers. As you browse an e-commerce site, your browsing history and the item you’re considering come together to create a risk profile. The site or payment platform may offer that profile and the details of the transaction to a handful of competing lenders so that at checkout you receive several offers for financing from different banks. If you have previously chosen to pay automatically with the most advantageous offer, the site could automatically select the credit source offering the best terms. From your perspective, your funding sources and credit card don’t have a fixed APR; the rate is variable and can change depending on your evolving real-time risk and the risk of the merchant.

Real-time analysis like this was, until recently, an impossible idea. But the innovations of ad networks like DoubleClick and Google AdSense have shown their potential and created an efficient market for advertising. A real-time approach to analytics in payment will undoubtedly lead to a wave of innovation among merchants and banks at the point of sale.

Excerpts from “ePayments: Emerging Platforms, Embracing Mobile and Confronting Identity” will be published throughout the week. You can download the full report here.